{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 设置 Matploblit 相关属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 为了使图像呈现的更清晰，需要添加以下设置\n",
    "## 第 1 行引入 matplotlib 库\n",
    "## 第 2 行将风格设置为近似R语言中的 ggplot，需要 matplotlib v1.41以上版本支持\n",
    "## 第 3 行设置插图，让它们在记事本中可见\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 鸢尾花数据集准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f_name = os.path.join(\"./data/\", \"iris.data\")\n",
    "df = pd.read_csv(f_name, names=[\"sepal length\", \"sepal width\", \"petal length\", \"petal width\", \"class\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用鸢尾花 Iris 数据集生成第一张图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115e99ef0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cdfda0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建了一张宽度为6英寸，高度为4英寸的一个插图\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.hist(df['petal width'], color='black')\n",
    "ax.set_ylabel('Count', fontsize=12)\n",
    "ax.set_xlabel('Width', fontsize=12)\n",
    "plt.title('Iris Petal Width', fontsize=14, y=1.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119230d68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2, figsize=(6,4))\n",
    "\n",
    "# First\n",
    "ax[0][0].hist(df[\"petal width\"], color=\"black\")\n",
    "ax[0][0].set_ylabel(\"Count\", fontsize=12)\n",
    "ax[0][0].set_xlabel(\"Width\", fontsize=12)\n",
    "ax[0][0].set_title(\"Iris Petal Width\", fontsize=14, y=1.01)\n",
    "\n",
    "# Second\n",
    "ax[0][1].hist(df[\"petal length\"], color=\"blue\")\n",
    "ax[0][1].set_ylabel(\"Count\", fontsize=12)\n",
    "ax[0][1].set_xlabel(\"Width\", fontsize=12)\n",
    "ax[0][1].set_title(\"Iris Petal Length\", fontsize=14, y=1.01)\n",
    "\n",
    "# Third\n",
    "ax[1][0].hist(df[\"sepal width\"], color=\"brown\")\n",
    "ax[1][0].set_ylabel(\"Count\", fontsize=12)\n",
    "ax[1][0].set_xlabel(\"Width\", fontsize=12)\n",
    "ax[1][0].set_title(\"Iris Sepal Width\", fontsize=14, y=1.01)\n",
    "\n",
    "# Fourth\n",
    "ax[1][1].hist(df[\"sepal length\"], color=\"gray\")\n",
    "ax[1][1].set_ylabel(\"Count\", fontsize=12)\n",
    "ax[1][1].set_xlabel(\"Width\", fontsize=12)\n",
    "ax[1][1].set_title(\"Iris Sepal Length\", fontsize=14, y=1.01)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1194d5080>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cdf8d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(df[\"petal width\"], df[\"petal length\"], color=\"green\")\n",
    "ax.set_ylabel(u\"Petal Length\")\n",
    "ax.set_xlabel(u\"Petal Width\")\n",
    "ax.set_title(\"Petal Scatterplot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119687ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195a4f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.plot(df[\"petal width\"], color=\"blue\")\n",
    "ax.set_ylabel(\"Petal Length\")\n",
    "ax.set_xlabel(\"Specimen Number\")\n",
    "ax.set_title(\"Petal Length Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 堆积条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119429208>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gpZdeYsCAAbRo0YKOHTvSvXv3Gs/bunVr+vTpA8Cuu+7KzJkz19i/ZMkSZs+ezY9+9CMA2rZtywYbbEAcx6tL9oMGDWLOnDmrS/fNRQFdREpSv379mDx5MlOmTGHZsmXstttuax2z4YYbNvq8ZWVlq0v6LVu2bPDgqfvvv59PP/2URx99lAkTJlBeXk5lZWWjXz8fCugiUpLatWtH9+7dGTZsWI2l82x7770348aNY9WqVcyfP5/nn38+p9fdaKON2HLLLRk/fjwAlZWVLFu2jMWLF1NeXk6rVq149tln1yrZN4eS6eXS1K3opaYhF6zI+qaiooITTzyRkSNH1nts//79mTx5Mj169KBTp07ssssubLLJJjm97o033sj555/P1VdfTVlZGbfccgtHHnkkJ5xwAr169WK33XZju+22y+nc+YiaudI+znU+dAX0Cg1dz4OG/ucnO/++/PLLnKozCm3p0qW0a9eOhQsXMmDAAB588EE6duzYLK/dkKH/UHPeJkP/81/gQkQkLU444QQ+//xzVq5cydChQ5stmDcXBXQRWW/885//LHQS1ik1ioqIpIQCuohISiigi4ikhAK6iEhKKKCLSEm64YYbOOigg+jduzd9+vThtddeq/XY0aNHM2fOnGZMXWGol4uI5K2hS6Q1VH3jVV555RUmTpzI+PHjadOmDQsXLlw982JNxowZw0477cQWW2zRpOksNiqhi0jJmTdvHh06dKBNmzYAdOjQgS222II333yTH//4xxx88MH85Cc/Ye7cuTzyyCO88cYbnH766fTp04dly5bxzDPP0LdvX3r16sWwYcNWz7ly+eWX06NHD3r37s1ll10GwOOPP86AAQPo27cvgwYNavYJtxpDAV1ESs4Pf/hDZs2axf7778/w4cN5/vnnWblyJSNGjGDUqFGMHz+eQYMGceWVVzJgwAB23313/vjHPzJhwgSiKOLss89m5MiRPPHEE1RVVXH33XezcOFCHn30USZNmsTEiRMZOnQoAN26dePhhx/m8ccf5/DDD+fmm28u8LuvnapcRKTktGvXjvHjx/Piiy/y3HPPceqppzJ06FDee+89jj76aCAsZF3TSNAPPviALl26sO222wIwcOBA7rrrLn72s5/Rpk0bzjnnHHr37k3v3r0BmD17Nqeeeirz5s1jxYoVdOnSpfneaCMpoItISWrZsiXdu3ene/fu7LTTTtx5553ssMMOPPzwwzmdr6ysjHHjxjF58mTGjRvHX/7yF8aMGcNFF13EkCFD6Nu3L8899xzXXnttE7+TpqMqFxEpOVOnTuXDDz9c/ffbb7/N9ttvz8KFC3nllVcAWLlyJe+99x4QSvRLliwBYNttt2XGjBlMmzYNgPvuu499992XpUuXsnjxYnr16sWll17KO++8A8AXX3yxujF1zJgxzfYec6ESuoiUnC+//JIRI0bwxRdfUFZWxjbbbMMf/vAHfvrTn3LxxRfzxRdf8NVXX3HSSSex4447YmZccMEFtG3bloceeohrr72WU045ha+++ordd9+d4447js8++4yf//znVFZWEscxl1xyCQDnnHMOp5xyCptuuin77bcfM2bMKPC7r52mzy0Rmj43P5o+Nz9pmT63kDR9bgYzK3QSCqquPrYiIqA6dBGR1FBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBeRknPUUUfx5JNPrrHt1ltv5YILLsjrvFdddRVPP/10o5/33HPPcfzxx+f12k2hZLotikjx6ty5c5Oe75NPPqlzf0VFBWPHjqVHjx6rt40dO5YRI0bUe+44jonjmBYt1i7P/vKXv2x0WouJSugiUnL69+/PE088sXp8xowZM5g7dy777LMPI0eO5JBDDqF3795cffXVq/cfcMABnHnmmfTs2ZNZs2Zx1lln0bNnT3r16sWoUaMAOOuss3jkkUcAeP311znssMPo3bs3/fv3Z8mSJSxfvpyzzz6bXr160bdvX5599tm10rZo0SJ+/vOf07t3bwYMGLB6CoHnnnuOPn360KdPH/r27bt6KoKmpBK6iJSc9u3b07VrVyZNmkS/fv0YO3Yshx56KE8//TTTpk1j3LhxxHHM4MGDeeGFF+jcuTPTpk3j+uuvZ8899+TNN99kzpw5/Pvf/wbg888/X+P8K1as4NRTT2XkyJF07dqVxYsX07ZtW2677TaiKOKJJ55g6tSpHHPMMTzzzDNrPPeaa65hl1124Y477mDy5MkMHTqUCRMmcPPNN3P55Zez9957s3Tp0tVzuTclldBFpCRVV7tAqG6pqKjgqaee4qmnnqJv377069ePDz74YPUkXFtttRV77rknAF26dOHjjz9mxIgRTJo0iY033niNc3/wwQd07NiRrl27ArDxxhtTVlbGyy+/zJFHHgnAdtttx1ZbbbXGJGEAL730Ej/+8Y8B2H///Vm0aBGLFy+mW7du/PrXv+b222/n888/p6ys6cvTCugiUpL69evH5MmTmTJlCsuWLWO33XYjjmNOP/10JkyYwIQJE3j22Wc55phjANaYH2WzzTZjwoQJ/OAHP+Cee+7h3HPPXefpPfPMM7nqqqtYvnw5FRUVTJ06tclfQwFdREpSu3bt6N69O8OGDaOiogKAHj16MHr0aJYuXQqExSlqmpRt4cKFrFq1iv79+3PeeecxZcqUNfZvu+22zJs3j9dffx2AJUuWUFVVRbdu3XjggQeAUIr/5JNPVi+UUW2fffbh/vvvB0K9eYcOHdh4442ZPn06O++8M6eddhq77777OgnoqkMXkZJVUVHBiSeeyMiRI4GwNN3777/PYYcdBoRS+U033UTLli3XeN7s2bMZNmwYq1atAmD48OFr7G/dujUjR45kxIgRLF++nLZt2zJ69GhOOOEEhg8fTq9evWjZsiXXXXfdWnXhw4YNW73qUdu2bbn++usBuOWWW3j22Wdp0aIFO+ywAwcddFCT50fJTJ/b1KuKl5oVK1Zo+tc8aPrc/Gj63Pw1x/S5qnIREUkJBXQRkZRQQBcRSQkFdBFptGZue1uv5JO3Cugi0mgtWrRoUAOfNE5VVVWNc8w0lLotikijtW3bluXLl1NZWUkU1dv5QoA2bdpQWVlZ6/7qCcPatm2b82sooItIo0VRxAYbbFDoZJSU5ug6W29AN7M7gAHAPHffJdnWARgNbANMB8zdF627ZIqISH0aUllzJ3Bw1rYLgCfcfXvgieRvEREpoHoDurs/DSzM2nw4cFfy/7uAiiZOl4iINFKudeibu/vs5P9zgM1rO9DMhgBDANyd8vLyHF9y/VZWVqa8y4PyLz/Kv/w1Rx7m3Sjq7rGZ1dpx0t1HAaOSP+NcGwXW97lcqqqqNBdJHjSXS36Uf/nLJw8bGv9y7fA418y2BEj+nZfjeUREpInkGtAfAk5I/n8CMLZpkiMiIrlqSLfFvwM9gHIzmwlcAlwBuJmdCHwE2LpMpIiI1K/egO7ux9Syq1cTp0VERPKguVxERFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCXK8nmymZ0NnATEwBTgZ+6+vCkSJiIijZNzCd3MOgNnAnu5+y5AS+DopkqYiIg0Tr5VLmXABmZWBmwIzMo/SSIikoucA7q7fwJcDXwMzAY+d/fHmyphIiLSOFEcxzk90czaA/cBg4DPgDHAP939r1nHDQGGALj7nitWrMjp9Vq3bp3T89Ji1apVVFVVFToZJausrEz5lwflX/7yycMk/kX1vkZOZw96A9PcfT6Amd0PdAfWCOjuPgoYlfwZL1iwIKcX69SpU+4pTYGqqipyzTuB8vJy5V8elH/5yycPGxr/8gnoHwP7mtmGwDKgF/BKHucTEZE85FOH/iLwT+A1QpfFFnxdEhcRkWaWVz90d78EuKSJ0iIiInnQSFERkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlCgrdAJESsHkyZMLnYSCqqioKHQSpAFUQhcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGU0AIX6wkt0KAFGiT9VEIXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCfVyWU+YWaGTUFArVqwodBJE1rm8ArqZbQbcBuwCxMDP3f35pkiYiIg0Tr5VLjcA4919J2B34N38kyQiIrnIuYRuZpsCBwKDAdx9BaD7WhGRAsmnyuXbwHzgL2a2O/AqMNTdlzZJykREpFHyCehlwB7AGe7+opndAFwAXJR5kJkNAYYAuDvl5eV5vOT6q6ysTHmXB+VffpR/+WuOPMwnoM8EZrr7i8nf/yQE9DW4+yhgVPJnvGDBgpxerHPnzjk9Ly0qKyvJNe8AOnXq1ISpKT1VVVV55d/6TvmXv/Ly8pzzsKHf35wbRd19DjDDzHZMNvUC3sn1fCIikp98+6GfAdxrZq2BD4Gf5Z8kERHJRV4B3d1fB/ZqorSIiEgeNPRfRCQlFNBFRFJCc7mINIDmwtGYwVKgErqISEoooIuIpIQCuohISiigi4ikhAK6iEhKKKCLiKSEArqISEoooIuIpIQCuohISiigi4ikhAK6iEhKKKCLiKSEArqISEoooIuIpIQCuohISiigi4ikhAK6iEhKKKCLiKSEArqISEoooIuIpIQCuohISiigi4ikhAK6iEhKKKCLiKSEArqISEoooIuIpIQCuohISiigi4ikhAK6iEhKKKCLiKSEArqISEoooIuIpIQCuohISiigi4ikhAK6iEhKKKCLiKSEArqISEoooIuIpERZoRMgUgqiKCp0EgqqsrKy0EmQBlAJXUQkJRTQRURSIu8qFzNrCbwCfOLuA/JPkoiI5KIpSuhDgXeb4DwiIpKHvAK6mW0F9Adua5rkiIhIrvItoV8PnAesaoK0iIhIHnKuQzezAcA8d3/VzHrUcdwQYAiAu1NeXp7rS67XysrKlHd5UP7lR/mXv+bIwyiO45yeaGa/B44DqoC2wCbA/e5+bB1Pi2fNmpXT63Xu3Dmn56VFZWUlCxYsyPn5nTp1asLUlJ4VK1bklX+6/vK7/gTKy8tzzsPk+1vvYIicS+juPhwYDpCU0M+tJ5iLiMg6pH7oIiIp0SRD/939SeDJpjiXiIjkRiV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCQV0EZGUUEAXEUkJBXQRkZRQQBcRSYmyQidAmkcURYVOQkFVVlYWOgki65xK6CIiKaGALiKSEgroIiIpoYAuIpISCugiIimhgC4ikhIK6CIiKaGALiKSEgroIiIpoYAuIpISCugiIimhgC4ikhIK6CIiKaHZFkVknZs8eXKhk1BwFRUV6/w1VEIXEUkJBXQRkZTIucrFzLYG7gY2B2JglLvf0FQJExGRxsmnhF4FnOPu3wX2BU4zs+82TbJERKSxcg7o7j7b3V9L/r8YeBcoYwGQAAAX4UlEQVTo3FQJExGRxmmSOnQz2wb4PvBiU5xPREQaL+9ui2a2EXAfcJa7f1HD/iHAEAB3p7y8PN+XXC+VlZUp7/Kg/MuP8i9/zZGHeQV0M2tFCOb3uvv9NR3j7qOAUcmf8YIFC/J5yfVWVVUVyrvcKf/yo/zLXz552KlTpwYdl3OVi5lFwO3Au+5+ba7nERGRppFPCX0/4Dhgipm9nmz7lbv/K/9kiYhIY+Uc0N19MhA1YVpERCQPGikqIpISCugiIimhgC4ikhIK6CIiKaGALiKSEgroIiIpoRWLRGSdM7NCJ6HgVqxYsc5fQyV0EZGUUEAXEUkJBXQRkZRQQBcRSQkFdBGRlFBAFxFJCXVbFJF1Loo0MWtlZeU6fw2V0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURSQgFdRCQlFNBFRFJCAV1EJCUU0EVEUqIsnyeb2cHADUBL4DZ3v6JJUiUiIo2WcwndzFoCfwJ+BHwXOMbMvttUCRMRkcbJp8qlGzDV3T909xXAP4DDmyZZIiLSWPlUuXQGZmT8PRPYJ/sgMxsCDAFwdzp16pTTi8VxnNPz0iTXvAPlHyj/8qX8y18+edgQ67xR1N1Hufte7r4XEOmR28PMXi10Gkr5ofxT/hX60QR5WK98AvonwNYZf2+VbBMRkQLIp8rlZWB7M/s2IZAfDfykSVIlIiKNlnMJ3d2rgNOBx4B3wyZ/u6kSJmsZVegElDjlX36Uf/lb53kYqbFCRCQdNFJURCQlFNBFRFJCAV1EJCUU0EVkNTNrUH9nqV0h8zCvybkkP2bWjTCFwsvu/mKh01OKlIf5MbO2wJHAge7+i0KnpxQVUx6qhN6MzKydmW2d/L878BAwAPiTme2cbNdnUgflYdMws75m9jfgHeCvwBZmtpG7xyqlN0wx5qEu/OZ1HtAz+X934C/ufjDwCnBWsl2fSd2Uhzkys++b2fVm9jpwF7AMGAFcCswDzk0ObVmYFBa/Ys9DXfjNIKPEuDnQI/n/QuCg5P9jgP1h9YAtyaI8zJ2ZHWdm7xFmRN0S+By4l/Dj+A93vwy4EjgFlH81KZU8VEBvXncAfc2sjPDLvszMNiCMtP2tmW1Y0NSVBuVh480F/gYMBC4CKt39XHf/1N1XJWsbdAemm9nmhUxoESuJPNRI0WZmZuOASmAb4A53/2OyvaW7f2VmkbvrQ6mD8rDxzKysutRoZjOAXwLPAIuAHwCXAH9191HKv5qVQh4qoDcTM2uR/JKXE+aN/w5hxacy4BBgDqGnxlcFTGZRUx7mJ+MH73jgVKAK+AZhbYObgOvdfUEh01jsij0PFdALxMxaEercTgQ+ItzSzSFcEK9WXziFTGOxUx7mJumB0R7oDcx390lm9g1gFbA0WYFM6lCseag69MI5EegAPEG4bRtA6P50fbJfv7T1Ux7mwN1jd1/o7g7MNrO7gfHA3cBEM+sL6v5Zl2LNQ5XQC8DMOgI3J49PgUfdvVOybxbwPXdfVMAkFj3lYf7MbAtgJOGuZhChO+g2hB/Evd19vurT61Zseahf4MJYBOwBvObubwBfJv1bDwBeAzYoaOpKg/Iwf90JS5udDswHNnP3B4H3gBOSYzTIqG5FlYcK6M0sqdddCfwPOCPZ/DdgEnArMN7dZxUqfaVAeZifjFGMfYE3knaG+4CTku0fA18AuPuq5k9h8SvWPFRAL5xrgd7JcPVrgOeAC6u74EmDKA9zUx2MxhH6VQP8ETjazO4D+gCjC5GwElKUeag69AIyMyP8ur+Xsa0z4VZtqbvfoJ4adVMe5sfM3gV+7+53m9kooBVhKPs8YC93f97Murv7c9XdRgua4CJUTHmogF4A2Y0kZrYpcChh2PBeQBvgGnf/ZYGSWPSUh/nJ6E99BKGr3UNAq+rudmb2E+Bq4EtgMaGBT1MCZCjGPFRAL5Bk6PpBwPGE27O2hC5PTpgOdkfCqLNnVMKsmfKwaZnZ9sBPgCMIP4hbAzcA1wFfqH96/Qqdh5oPvQDM7DrgWGBD4AXCoJhh7v5scshkM/s1Yea2Z1B/6rUoD5uOmW0E/AaoAN4mzJfzd8LEU3tr9Gj9iiUP1ShaGIuBc929HaG7038yAlH1hPmdgPdBPQ1qoTxsOrsQqqmOB34GjHT3+cDvCJNNqeti/YoiD1XlUmBm9m3gv8D3k03fBH5IuGU7yd1fLVTaSoXyMD9JI/LH7l7nHN4aZFS7YslDVbkUSDIkOHL3aWb2W+B+wvzenxEaWEYoENVNeZi/pNfFJ2Y2ysx2IVRdrXL3pclUxD8FtnL3Swhd9RTQsxRTHqrKpUCSKoDqaoDfEyb5+T/CyidHu/s4M9vSzHY3s9YFSmZRUx42iergcjahGus64E4zO8ndvyTMM3+SmXVMZrpU9cvaiiYPFdALqPrWy92r3H0mMBvYAeiSlD7LCHMuD4HQTapQaS1WysP8ZOTfcsKo222B14HTzKyvu08G/kOYCA0UM9ZSTHmoD6dIWFh15/fAcOAKQnXBDOABYGhymBr26qA8zE3GjIDfASa5+++ARwl3OxCGsH8DQF0/a1YseaiAXjy2Aw4AjiYMaf8FgLvfB7Qys++oQapeysPcVFcB3A30T2ayvAjoYWa/IUxAdS9oSt06FEUeqpdLETGzTwlDhaeZ2cvAPcmu4wBz92nJcRqCXQvlYX7MrIJwl7MM2A14BfiDu9+fcUxrYAd3f6swqSxuhcxD/doWgYx63RuBX5nZsYSL4GxCQ98fkwBVBupTXRPlYX4yGuq6E/rvjwf2dPd9qwORme1qZqcDtwB/MLONC5Pa4lQMeahui8WhOrjcSFiw4QZgKvA48BhhRjf86wVqfwzEmb/4ojzMU3V3ukvd/bzqjWb2LaAHYSqFTsAK4EPCzJatmj+ZRa3geagqlyJjZiOBt4CH3f3jjO3fB84kDJbZELjc3S8tSCKLnPIwP2a2CdCLEIC2JwSqWYQh7a8Cb7n7ssKlsPgVKg8V0ItETSPIzKwToRvU8YQW8gnAbcCEpH+rZFAeNg0z6w9cQFif9X3CClBvuPunWcdp5GgtCpWHCuhFyMzOIPSb/i7h1/x24AF3n1fQhJUQ5WHuktGNBwMvJ90+M/cpiDdAofJQAb2IVH/QZvY3wu3Zn919aqHTVUqUh01PQTx/mXlY3W1xnTTMx3GsR5E8Bg4c2KIRx0YDBw5sWeg0F9ujMXmoR715GdX0/4xt7QcOHLjPwIEDNy90Wov1UVO+rcuHSuglRgs15EelzfyYWTugC6Eqay9Cw9+/1Lhct6wS+haEQUf/cffbmvKaVLfFEpDcosXuHlcH82TK2ArCyMiBCvL1y/pS7ezu7xY6TaXAzFoBnQkrQO0N7AlsQZhV8GZgbOFSV/yyrrvvEJZJ3JJkEWk1iq4nskvjZtYeOCR5fAuYA0wDrnP3WYVJZWkxsw7AOYRlwg5IJvSSGiSB/ABgZ2A/wjwlC4CngPvd/YMCJq+kJAuunAwMI+Tf1etipK0CepFLSue9gcMIw4iXA28CLxP6Wn/s7osLl8LiVj3EPxlJeggwGNgEuMHdH1EVVt3M7EFgc0J3zzHuPiVrfxnwlaqxamdmuxIKEfsBvwb+QViMpRyY7+6vmFmZN8EC0qpyKWJmdjBhToiFwHTgLsLostnu/lkBk1YyMnoSHE3oRvY6cJu7z06CvYJ5DTJ+6C4Aprp7lZl9z8yGAR2BDoQRkbozrF8FYRzEY4Q2h98SpnnekVBA69QUwRw0l0tRypgT4k3CCvaXAL9w99uT7R2T22Gph5ltZmYvECbp6gqsJMyAdxPwipntmRyn70KG6h86d/9vEswPIRQuLiPUn8fAPWZ2AGie+ZpkXFN/BvYnLLzyB8Kd9p3Aw8AxTfmauoiLUMaE+bOAa5MJ8vuZ2RPAQ4SL4qGkBK8vUx2SO5mPgecJg4sWAj8ilJi6Jg9N1lWHpPAwAniEUP/7qrufQrhbPL+QaStm1deUu8939+eAN4BdgWcJc7tc4u5PmVmbpnpNBfQilrSOV5rZ9oSVxN8BWhIWa7gbuEV1wLXLKCGd6O77ufv1wEvAFOByYOeMux6p3YHAp+4+irC82hnJ9jHAJsl1qmuwDsncLhcSfgDvJPxA9jOz3wHDrImWpVNAL2IZDU0HE9YqPAtoC2zi7n8H5gKDQFUGNckoIS02s7ZmdgxhObpOwDPu/p6ZdUpmw8PMWjbVFytl3gL2AXD3icCmZnY8oSrhiUImrIS0IgT0MuCHwNPA4YSZF3ci9FrDzFonXZJzoiBQxDKCSw/g+aQUNB44Ntk+mzCJvqoM6jeMsGLM4UA74Agz+w/wAXAShHpj9dZYU9JwPBeYamZnJ5vvIgyMeZqwcIPyrA5JHn5KWGv0J8AF7t7Z3Q9x91+7+wlAdSeHDoTSe07Uy6W4tQC+IrSODyZMin8T8KKZ7UEorU8sWOpKQEaV1GuEudEfIJSKvkFo4HuKr1dtx8wGAHPd/eUCJLcYVRcqfk+oIuhEaMN5kzA98TIz6wa86WGRZMmScac4LXO7mW1LGHG7B7CrmZ3o7nPM7N9mtpG7L2nsa6kfeglIGqWmABe6+31m9g/CorMXuPvCwqau9NQwYKsnUObuj5vZEICkvlgymNlm1d1lk+Hr+xMGHnUHzk0a+LS0Xx3M7JuEPui7EoL55sDnwEzgRnf/JJ/zq4Re5JIvyEoz+zVQmWw+NmPlnX2AH7v7eZqnpGHc/Ssz+y6hobkv8E1CNcLjwK1A6wImr5hVmdlRhEUbtiPcQX6TcKd4EPCUgnm9DgFOAD4F/kvoefVa9t1Nrt9lldBLUNKIdzph4eOOwLvAD9z9i4ImrMgloxrPIzQydyH0GnqEUG01R/lXt+RH8GbC9fY/QjXWO4QqrEnAYHd/U6X0tWVM67w9oU3skWRw217A94ClhMGD/0kKHDkFdJXQS4iZ/YIwcq8TofvdrwglpE0JDXvXNtUQ4jRKBsjsQwg+D7n7q7B6MYLVAUh3OjVz93eSbnbvZs+BY2aLgB8T6taVd1kyxpa8T1jBCDO7ANiX0LGhG2FupgeBq4A2ZlbZ2OtQJfQSUF3na2bnAZsBt7j7Rxn7tyEsb7VpodJY7DJKSJsCi5P5XQYR7nQ2JZQ6H1e/9IZLBsR0APoRJp46PxkEJ3VIuhj/lnCHPRyY7O7Tk8blJ919w1zPrRJ6aahuJf9DLfu7AR+bWRfPWBRZvpZRQvocwMyuIgSicYS68/2BC8zsE3cfX7CElgAzKyd0wfsR0J/wg/h3wlJ/Ur9NCA3JR7n7i7C6i/I7wF1mthWwO2EQ0l8b00CvgF4CarrtSurRexAWGegB3Klg3jBm9kOgJ6FE+WhSep9kZssIk3iNT3ojdHb31wua2OL0O8KAtlcJ3WjH+DpYwT6t3P2zpD1ik4xtsZldRJhVtRdh0q4XCUG+wVTlUmKSCfLPA9oTehfMJPRTf1j1vnXLqHYZAhzt7j0z9rUjjOSbm2zqTyhF7eLu05s9sUUoo+rvB8Aid/+vmX2DUFL/FmERjNvc/bWCJrSIVbdxmdkRhCkVVhB6Cw0AtiIsFvIg8KJnLS7dEBopWnrmEwL5i8BF7n4aYXDMLknjHrDGKFNZ28PAjma2q5l928x2ItQBn0cYpTeYMJnXbkndpvIyqK62ej4J5rsSBrsdQ5hBsAvwJzM7DjRpXE0yOiw8Tig0/IJQ3fdnoJu7H0sYc9LBzHY3s72h4d9nldBLkJlt7u5zky/UrwhfpnmEH2h39z+p61jN7OsFL44iNEhtSMi3loSZLG9z90bd5q5PksDSktBXfxKhq92F7j412X8yYZzED9VbqGYZ1+AxwCeE0nhlMoCwE3AmYYzEROAzdx/S0HOrDr0EJcF8S8Jw7PcJgxOWE255/2RmD7j7LH2halSdHwsJo/XuJPQJfqj6ADMbTZji9GhgiK+DpcJKkZntSKjj/TOwC9DK3QdlHfY/4L9m1s7dlzZ3GktBRkHLkyqsLcxsX8IUANsS6tY3JHRN/mdjzq0qlxJjZpsn/z2ZUEq6wd0nuvtkdx9N+FXvXrAEFrmM3i7/Btq7+5DqYG5mh5rZHYRb4V8SJp/StLBrOs3DkodvANtnzgyYBKUbCf3RexQmeaUjCeZtCb1ZTiYE87eA0wiT8G2UVPm1gIZVuyiglxAzu5VkZkDC6LJnM+t4zex7hCC/VYGSWDKSu5elZra1mV1nZm8TJp1aShjY0dLdLyAMzxbA3d8DWpjZIHf/klD/e5WZjTOzycBIYBYhH+fWcSr52hDCmsFXAv/n7r9PeqtdQxKfk+qZvfh6orRaqQ69hJjZZYSGugoLS4KdQ1hw4C3C7dowQul8AfA9d59fsMSWADNrTWiD2IPQs+B5YKaH+dPfAw5xrWy/BjOrnnJiAbAxYZ7vFoSeVpOB1919npn1Jsw9tBnwftKIqirAREY9el/CpFw71XBMe0Je9ySsP3qih5WPaqWAXkIsTF36CmGN0WcIv+zXEX65lxOmhr3W3f9TPf2mmXVMvmBqJK2BmX0ADHd3z9reDfiQME91f2Avd7+oAEksKknPlT0JjXbvA1MJfaU/TALUFoQZBO8iNPDNAka7++8LlOSiZ2YPAUckVTAdCBN49SQsj/g+YTqFd4AX3H12XedSQC8xZnYmYRXxbwDbEOrMb3f3f2UcswPwF+A9YAegl7tXrn229VdGf+BbgI/d/XdZ+3cjdMc7kbAgxjjgJ5onp2ZJdd+BhDvEbxF6wuxI6Ir3oUrna8vMEzPrBRxB6LG2lNB4vwXhuvuNu69oyDnVy6XEuPuNZnYvYfrSV3zNeb37AocSlrTqRujKeBOh1VzVL2uqzreLgW/D6tG3xxJuc3cAZgBnuvs/CpLCIpa020RJqbwncDWwBHgZGOXuz5jZXMKc3x8qmK8tI5hvTci/5wm9rt4iXHuLgQmEqqzHzKyVu6+s65wK6CXIw3JWn8LqKWGPIJQk2wLTCHN6fwjslPR8WWtRh/VdRm+XuWb2fTN7jdDQPBX4I2EEX8fkQdIbodGz36VVkg/VefF/hDVah2YddjIhP7GwwtYu7n5386WyNLj7jGT08lxCG07mzJ//I3y/HwPqvTtUlUuJSybyuYEwuuwZYKq7f2RmGwNPAvuomqBmGQ1TpxAaRn+bOdzazDYirCbTtr6S0fooYyqAywlVK72z9m9JmGvoEEK9emtCw556wNQhue62JqwGdQpwibs/0pDnqoRe+pYT5oE42ZPl6JIv2mIz65vUE3cDLgXecvfzCpjWouJfr/V4Sy2HfJ/QIPUdQnuErKm6JHktMBBWz8S4X/LYk9AD5hXCVBXvEXrHSA2SeZq2I+RbN8J8TWMJ1S4NohJ6CcsoYf6L0PVpfMa+zsBPgTMIF8aTwPXurkWl62BmmxB6tfQD+hCmhT1f1VV1S0rj/0doj9iQMBJ3c2Al8L+kT7/UwczOJ7TffESYn+kBDwtiNJhK6Olwsrt/ktTzngScCuyc7LsJ+L27zylY6kqAmXUkDCjamhCE3iIM+x9X0ISVju0I1VbPEe5qXvNkwWMz+8TMbkquUXWfzZKRJ48SuiY+leu5VEJPCTN7ADicMJfGPcAYQiNfT0JDy5Wm5elqlfTa+BNhSPvrwAfuruqBRjCzHZPRpNV/tyR0+XyZUA+s3kLrmEroJS6j98pDhCHXL2aUgP5nZvOBOwhDi1VtUAP7ep70C4EvVL2Sm+pgnvS82pRQ/fIzQt25qvqagUroKZY0slwClAOm2e9kXUomkdqFMLjo8OT/7xBGLz9WyLStLxTQUyaZ/2E/QpenbQhzaVzk7i8VMl2yfjCzFwgFiL8Bd1fPky7NQ1UuKZKM2PslYejwZ4S6y3+7lgSTdSyj6m+Qu39U6PSsr1RCT4GMOuAtgLMIQ4j/41o0WmS9ooCeMpoESWT9pYAuIpISWrFIRCQlFNBFRFJCAV1EJCUU0EVEUkIBXUQkJRTQRURS4v8BauiLDlfK8t4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c71898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "bar_width = 0.8\n",
    "labels = [x for x in df.columns if 'length' in x or 'width' in x]\n",
    "ver_y = [df[df['class'] == 'Iris-versicolor'][x].mean() for x in labels]\n",
    "vir_y = [df[df['class'] == 'Iris-virginica'][x].mean() for x in labels]\n",
    "set_y = [df[df['class'] == 'Iris-setosa'][x].mean() for x in labels]\n",
    "x = np.arange(len(labels))\n",
    "## bottom 参数表示，把该序列y点最小值设置设置为该序列中y点的最大值\n",
    "ax.bar(x, vir_y, bar_width, bottom=set_y, color='darkgrey')\n",
    "ax.bar(x, set_y, bar_width, bottom=ver_y, color='white')\n",
    "ax.bar(x, ver_y, bar_width, color='black')\n",
    "ax.set_xticks(x + (bar_width / 2))\n",
    "ax.set_xticklabels(labels, rotation=-70, fontsize=12)\n",
    "ax.set_title(\"Mean Feature Measurement By Class\", y=1.01)\n",
    "ax.legend([\"Virginica\", \"Setosa\", \"Versicolos\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
